Diffusion-based generative models have proven to be highly effective in various domains of synthesis. In this work, we propose a conditional paradigm utilizing the denoising diffusion probabilistic model (DDPM) to address the challenge of realistic and diverse action-conditioned 3D skeleton-based motion generation. The proposed method leverages bidirectional Markov chains to generate samples by inferring the reversed Markov chain based on the learned distribution mapping during the forward diffusion process. To the best of our knowledge, our work is the first to employ DDPM to synthesize a variable number of motion sequences conditioned on a categorical action. The proposed method is evaluated on the NTU RGB+D dataset and the NTU RGB+D two-person dataset, showing significant improvements over state-of-the-art motion generation methods.

The mainstream motion prediction methods usually focus on short-term prediction, and their predicted long-term motions often fall into an average pose, i.e., the freezing forecasting problem [ 27 ]. To mitigate this problem, we propose a novel Bidirectional Transformer-based Generative Adversarial Network (BiTGAN) for long-term human motion prediction. The bidirectional setup leads to consistent and smooth generation in both forward and backward directions. Besides, to make full use of the history motions, we split them into two parts. The first part is fed to the Transformer encoder in our BiTGAN while the second part is used as the decoder input. This strategy can alleviate the exposure problem [ 37 ]. Additionally, to better maintain both the local (i.e., frame-level pose) and global (i.e., video-level semantic) similarities between the predicted motion sequence and the real one, the soft dynamic time warping (Soft-DTW) loss is introduced into the generator. Finally, we utilize a dual-discriminator to distinguish the predicted sequence at both frame and sequence levels. Extensive experiments on the public Human3.6M dataset demonstrate that our proposed BiTGAN achieves state-of-the-art performance on long-term (4 s ) human motion prediction, and reduces the average error of all actions by 4%.

Modern saliency detection models are based on the encoder-decoder framework and they use different strategies to fuse the multi-level features between the encoder and decoder to boost representation power. Motivated by recent work in implicit modelling, we propose to introduce an implicit function to simulate the equilibrium state of the feature pyramid at infinite depths. We question the existence of the ideal equilibrium and thus propose a quasi-equilibrium model by taking the first-order derivative into the black-box root solver using Taylor expansion. It models more realistic convergence states and significantly improves the network performance. We also propose a differentiable edge extractor that directly extracts edges from the saliency masks. By optimizing the extracted edges, the generated saliency masks are naturally optimized on contour constraints and the non-deterministic predictions are removed. We evaluate the proposed methodology on five public datasets and extensive experiments show that our method achieves new state-of-the-art performances on six metrics across datasets.

Since the superiority of Transformer in learning long-term dependency, the sign language Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and Translation (SLT). However, there are several issues with the Transformer that prevent it from better sign language understanding. The first issue is that the self-attention mechanism learns sign video representation in a frame-wise manner, neglecting the temporal semantic structure of sign gestures. Secondly, the attention mechanism with absolute position encoding is direction and distance unaware, thus limiting its ability. To address these issues, we propose a new model architecture, namely PiSLTRc, with two distinctive characteristics: (i) content-aware and position-aware convolution layers. Specifically, we explicitly select relevant features using a novel content-aware neighborhood gathering method. Then we aggregate these features with position-informed temporal convolution layers, thus generating robust neighborhood-enhanced sign representation. (ii) injecting the relative position information to the attention mechanism in the encoder, decoder, and even encoder-decoder cross attention. Compared with the vanilla Transformer model, our model performs consistently better on three large-scale sign language benchmarks: PHOENIX-2014, PHOENIX-2014-T and CSL. Furthermore, extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on translation quality with$+1.6$BLEU improvements.